knitr document van Steensel lab
TF reporter barcode processing - Deep P53/GR scan - stimulation 1
Introduction
18,000 TF reporters on pMT02 were transfected into mESCs, U2OS & A549, sequencing data yielded barcode counts of these experiments. These counts will be processed in this script.
Analysis
Data exploration
1: Most samples have high matched read counts, but some seem to have lower counts:
High unmatched counts:
- A549_Dex-100_rep1/3
Low matched counts:
- MCF7-KO_DMSO_rep1/2
- MCF7-KO_Nutlin_rep2/3 (maybe also 1)
- MCF7-WT_DMSO_rep2/3 - MCF7-WT_Nutlin_rep3 - mES_N2B27-HQ_rep1 (maybe also 2)
- mES_N2B27-RA_rep1/2
-> Remove everything with less than 1700 matched barcodes with more than 500 rpm’s
2: Samples with high counts usually also have a high matched barcode percentage.
3: Barcode counts diverge from pDNA data for almost all samples (except MCF7-WT-DMSO-rep2, and maybe mES_N2B27-RA_rep3) - with this we can exclude that we mainly sequenced barcode from pDNA.
4: Bc counts match with insert-seq -> The GC bias is also present here. I should possibly correct for this.
-> Based on these figures I will exclude all samples that have less than 1700 barcodes with at least 500 normalized counts.
Normalization of barcode counts:
Divide cDNA barcode counts through pDNA barcode counts, if more than 30 pDNA counts for that barcode
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Calculate correlations between technical replicates
Data quality plots - correlation between replicates
Session Info
## [1] "Run time: 9.051943 mins"
## [1] "/DATA/usr/m.trauernicht/projects/SuRE_deep_scan_trp53_gr/stimulation_1"
## [1] "Thu Nov 26 10:19:56 2020"
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.7 LTS
##
## Matrix products: default
## BLAS: /usr/lib/libblas/libblas.so.3.6.0
## LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
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## locale:
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## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid parallel stats graphics grDevices utils datasets
## [8] methods base
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## other attached packages:
## [1] ggbiplot_0.55 scales_1.1.0 factoextra_1.0.7
## [4] shiny_1.4.0 pheatmap_1.0.12 gridExtra_2.3
## [7] RColorBrewer_1.1-2 readr_1.3.1 haven_2.2.0
## [10] ggbeeswarm_0.6.0 plotly_4.9.2.1 tibble_3.0.1
## [13] dplyr_0.8.5 vwr_0.3.0 latticeExtra_0.6-29
## [16] lattice_0.20-38 stringdist_0.9.5.5 GGally_1.5.0
## [19] ggpubr_0.2.5 magrittr_1.5 ggplot2_3.3.0
## [22] stringr_1.4.0 plyr_1.8.6 data.table_1.12.8
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## loaded via a namespace (and not attached):
## [1] httr_1.4.1 tidyr_1.0.0 jsonlite_1.7.1 viridisLite_0.3.0
## [5] splines_3.6.3 prettydoc_0.4.0 assertthat_0.2.1 vipor_0.4.5
## [9] yaml_2.2.1 ggrepel_0.8.1 pillar_1.4.3 glue_1.4.2
## [13] digest_0.6.27 promises_1.1.1 ggsignif_0.6.0 colorspace_1.4-1
## [17] Matrix_1.2-18 htmltools_0.5.0 httpuv_1.5.4 pkgconfig_2.0.3
## [21] purrr_0.3.3 xtable_1.8-4 jpeg_0.1-8.1 later_1.1.0.1
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## [29] lazyeval_0.2.2 crayon_1.3.4 mime_0.9 evaluate_0.14
## [33] nlme_3.1-143 forcats_0.4.0 beeswarm_0.2.3 tools_3.6.3
## [37] hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0 compiler_3.6.3
## [41] rlang_0.4.8 htmlwidgets_1.5.2 crosstalk_1.0.0 labeling_0.3
## [45] rmarkdown_2.5 gtable_0.3.0 reshape_0.8.8 reshape2_1.4.4
## [49] R6_2.5.0 knitr_1.30 fastmap_1.0.1 stringi_1.5.3
## [53] Rcpp_1.0.5 vctrs_0.2.4 png_0.1-7 tidyselect_1.1.0
## [57] xfun_0.19